Fact Constellation is a schema for representing multidimensional model. It is a collection of multiple fact tables having some common dimension tables. It can be viewed as a collection of several star schemas and hence, also known as Galaxy schema. It is one of the widely used schema for Data warehouse designing and it is much more complex than star and snowflake schema. For complex systems, we require fact constellations.
Here, the pink coloured Dimension tables are the common ones among both the star schemas. Green coloured fact tables are the fact tables of their respective star schemas.
In above demonstration:
- Placement is a fact table having attributes: (Stud_roll, Company_id, TPO_id) with facts: (Number of students eligible, Number of students placed).
- Workshop is a fact table having attributes: (Stud_roll, Institute_id, TPO_id) with facts: (Number of students selected, Number of students attended the workshop).
- Company is a dimension table having attributes: (Company_id, Name, Offer_package).
- Student is a dimension table having attributes: (Student_roll, Name, CGPA).
- TPO is a dimension table having attributes: (TPO_id, Name, Age).
- Training Institute is a dimension table having attributes: (Institute_id, Name, Full_course_fee).
So, there are two fact tables namely, Placement and Workshop which are part of two different star schemas having dimension tables – Company, Student and TPO in Star schema with fact table Placement and dimension tables – Training Institute, Student and TPO in Star schema with fact table Workshop. Both the star schema have two dimension tables common and hence, forming a fact constellation or galaxy schema.
Advantage: Provides a flexible schema.
Disadvantage: It is much more complex and hence, hard to implement and maintain.
- Difference between Data Warehouse and Data Mart
- Data Warehouse Architecture
- ETL Process in Data Warehouse
- Characteristics and Functions of Data warehouse
- Building a Data Warehouse in DBMS
- Snowflake Schema in Data Warehouse Model
- Star Schema in Data Warehouse modeling
- Characteristics of Biological Data (Genome Data Management)
- Difference between a Data Analyst and a Data Scientist
- Difference between Data Warehousing and Data Mining
- Types of Sources of Data in Data Mining
- Data Preprocessing in Data Mining
- Data Abstraction and Data Independence
- Data Integration in Data Mining
- Data Normalization in Data Mining
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